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Green AI

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Jul 26 2019 hardmaru

Green AI 🌳 “We want to shift the balance towards the Green AI option — to ensure that any inspired undergraduate with a laptop has the opportunity to write high-quality papers that could be accepted at premier research conferences.” 👩🏻‍💻 https://arxiv.org/abs/1907.10597 https://t.co/L8e1EqiQXD
8 replies, 586 likes


Nov 11 2019 Andrej Karpathy

💻🧠+🌍🌳 recent reads: Green AI vs Red AI https://arxiv.org/abs/1907.10597 and "Tackling Climate Change with Machine Learning" https://www.reddit.com/r/MachineLearning/comments/da30mv/r_tackling_climate_change_with_machine_learning/ https://t.co/8yRj6g7HIr
8 replies, 528 likes


Jul 25 2019 Roy Schwartz

The focus on SOTA has caused a dramatic increase in the cost of AI, leading to environmental tolls and inclusiveness issues. We advocate research on efficiency in addition to accuracy (#greenai). Work w/ @JesseDodge @nlpnoah and @etzioni at @allen_ai https://arxiv.org/abs/1907.10597
1 replies, 148 likes


Jul 26 2019 Taco Cohen

Green AI: "[Deep Learning] computations have a surprisingly large carbon footprint. [...] This position paper advocates a practical solution by making efficiency an evaluation criterion for research along-side accuracy and related measures" https://arxiv.org/abs/1907.10597
3 replies, 77 likes


Jul 26 2019 samim

The greenest form of "AI" is "No AI". Let's ask ourselves earnestly, does this task really need to be computerized & automated - or is it possibly healthier & saner for humans, flora & fauna to do the task? There is no free lunch - every augmentation leads to amputation.
3 replies, 76 likes


Nov 11 2019 Leo Dirac

I don't see a fundamental problem using massive compute to advance AI - research means pushing what's possible with today's tech to inform tomorrow. But I fully support the proposal by @etzioni and others to publish compute cost and efficiency in papers.
4 replies, 31 likes


Jul 27 2019 Tim Miller

Great initiative: use energy efficiency as an evaluation metric for ML research. Focus more on new ideas than larger computing resources.
0 replies, 23 likes


Jul 25 2019 Tom Simonite

"We propose reporting the financial cost or 'price tag' of developing, training, and running [machine learning] models" — @etzioni @JesseDodge @nlpnoah @royschwartz02 say researchers should disclose how much computing power they use to encourage greener AI https://arxiv.org/abs/1907.10597
0 replies, 15 likes


Nov 11 2019 Parisa Rashidi

The amount of compute used to train deep learning models has increased 300,000x in 6 years. The community should take into account not just AUC and precision, but also the carbon footprint. Paper by @etzioni @nlpnoah et al. https://arxiv.org/abs/1907.10597 https://t.co/HXUVV8aOQx
1 replies, 14 likes


Aug 02 2019 Ethem Alpaydın

Green AI: "...making efficiency an evaluation criterion for research alongside accuracy and related measures." https://arxiv.org/abs/1907.10597
0 replies, 14 likes


Oct 04 2019 Ant Kennedy

Interesting read from @allen_ai https://arxiv.org/abs/1907.10597 around introducing efficiency as an evaluation criterion for your models. Something we should all be considering especially given training a model can produce more CO₂ than an a lifetime of car travel 😨 #GreenAI
1 replies, 11 likes


Jul 25 2019 Ulli Waltinger

#GreenAI needs to be an emerging trend as the computations required for #deeplearning research have been doubling every few months, resulting in an estimated 300,000x increase from 2012 to 2018 #AISustainabilityQuest https://arxiv.org/abs/1907.10597 https://t.co/usSD3bPfgV
0 replies, 11 likes


Oct 04 2019 Ant Kennedy

@DanLarremore @anne_e_currie There are some interesting ideas emerging around using efficiency as a metric when training https://arxiv.org/abs/1907.10597
0 replies, 11 likes


Aug 15 2019 Peter Steinbach

For me, a long awaited and much needed initiative. Can't wait to read the paper. The abstract already made my eyes wet with anxiety. #Green500
0 replies, 8 likes


Aug 15 2019 Séb 🌐

The amount of compute used to train deep learning models has increased 300,000x in 6 years, leaving a large carbon footprint. This paper advocates making 'efficiency' an evaluation criterion for research alongside accuracy and related measures: https://arxiv.org/pdf/1907.10597.pdf https://t.co/NWNbEMgACh
1 replies, 5 likes


Jul 27 2019 Giorgio Patrini 🛡️👾

Let's hope this becomes a by-default reference in empirical papers in ML It's easy to misunderstand why this is important. *It isn't* about limiting comp resources for empirical research. It's about giving guidelines to practitioners who'll train those models thousands of time
0 replies, 4 likes


Sep 20 2019 Victor Sanh

If I had to pinpoint ONE reading for this we: Green AI (https://arxiv.org/pdf/1907.10597.pdf) by @royschwartz02 @JesseDodge @nlpnoah @etzioni. It advocates that efficiency should be a metric we chase just like accuracy.
1 replies, 4 likes


Jul 26 2019 Meg White

In today's batch of ML papers: "The computations required for deep learning research have been doubling every few months, resulting in an estimated 300,000x increase from 2012 to 2018. [...] Our goal is to make AI both greener and more inclusive" https://arxiv.org/abs/1907.10597
0 replies, 4 likes


Sep 12 2019 Victoire Louis

And here is the paper @MrsCaroline_C thanks for the recommandation https://arxiv.org/pdf/1907.10597.pdf Machine Learning is great ... and what about the environment ? #ClimateChange #MachineLearning #Innovation
0 replies, 4 likes


Oct 08 2019 Jim Schwoebel

https://www.technologyreview.com/s/613630/training-a-single-ai-model-can... something to be conscious of when working on models! https://arxiv.org/pdf/1907.10597.pdf -- Alle.. (sent from @Protea_app)
0 replies, 4 likes


Jul 26 2019 Luca Soldaini

Very nice proposition paper; using # of floating point operations seems to be a decent standard to approximate computational cost of a model, although I'm not convinced it captures the intuitive notion of "efficiency", which is imho "do the best with the resources you have".
1 replies, 4 likes


Aug 26 2019 Josie Young [🏳️‍🌈 ally]

@johnchavens @deeplearningai_ newsletter recently had a report from @allen_ai on measuring the carbon footprint of AI - really useful and a great start for building a green AI toolset! https://arxiv.org/abs/1907.10597
0 replies, 4 likes


Oct 08 2019 Titus von der Malsburg

Paper advocating for green AI that takes energy efficiency into account: https://arxiv.org/abs/1907.10597
0 replies, 4 likes


Jul 25 2019 Beliz Gunel

Focusing *only* on SOTA accuracy gives a clear edge to organizations that have the resources to train these fat models, creating barriers to participation in certain types of NLP research. Great review!
0 replies, 3 likes


Oct 03 2019 anjali 🚀 (but scary 🎃)

New research from @allen_ai gives the ML community concrete ways to change how we measure success -- benchmarking energy consumption will also help us develop more accessible models See "Green AI" by @royschwartz02 @JesseDodge @nlpnoah @etzioni https://arxiv.org/pdf/1907.10597.pdf
1 replies, 3 likes


Nov 11 2019 Spencer Dixon 🤖

I’ve talked a lot about tech giants and how in the future they’ll move into the #conservation world. Here’s yet another step in that direction. Google, Microsoft and Deepmind’s recent paper ‘Tackling Climate Change with Machine Learning’ https://arxiv.org/abs/1907.10597
0 replies, 2 likes


Jul 26 2019 Noah Smith

#greenai
0 replies, 2 likes


Aug 19 2019 digital dynamics

- Green AI - "This position paper advocates a practical solution by making efficiency an evaluation criterion for research alongside accuracy and related measures." https://arxiv.org/abs/1907.10597 #ethicalAI #responsibleAI #AI
0 replies, 2 likes


Aug 15 2019 Poilvet Eric

The computations required for deep learning research have been doubling every few months, resulting in an estimated 300,000x increase from 2012 to 2018. #Green #AI is a must. Interesting article via ⁦@deeplearningai_⁩ https://arxiv.org/abs/1907.10597?utm_campaign=The%20Batch%20081419%20MLY%20Intro&utm_source=hs_email&utm_medium=email&utm_content=75698304&_hsenc=p2ANqtz-8fEnmueToUyiSHgzO_7c6p_XlDqKxUhPsyNsbE-Z-C7K-NkUjeqCCKMBu2-ln9mo4RQ7-KPzWCeVC6gi41ia5cQamosA&_hsmi=75698304
0 replies, 2 likes


Oct 04 2019 aDynamics

#Green #AI https://arxiv.org/abs/1907.10597 #arXiv
0 replies, 1 likes


Oct 01 2019 David Schatsky

The computations required for deep learning research have been doubling every few months, resulting in an estimated 300,000x increase from 2012 to 2018 https://arxiv.org/pdf/1907.10597.pdf
0 replies, 1 likes


Oct 03 2019 Kostas Stathoulopoulos

@CassieRobinson That's why the energy efficiency of developing, training and running a model should be an evaluation criterion of research: https://arxiv.org/abs/1907.10597
0 replies, 1 likes


Jul 26 2019 Romain Rivière

Computations required for deep learning research have been doubling every few months, resulting in an estimated 300,000x increase from 2012 to 2018... https://arxiv.org/pdf/1907.10597.pdf #GreenAI #NLP #efficiency
0 replies, 1 likes


Sep 30 2019 Alex J. Champandard

8/ Metrics. Let's start measuring, reporting and comparing training times in papers. Beyond that, is there any interest in a semi-automated competition to create "Green" models whose innovation does not merely rely on more data or compute? 🌱 https://arxiv.org/abs/1907.10597
0 replies, 1 likes


Jul 25 2019 JuliaGo

Curious to see what the findings of our participants will be after the next 3 days @ the #nexushackathon where we will have one track regarding #co2 footprint of deep learning 👀 @HackathonNexus
0 replies, 1 likes


Jul 26 2019 BISHAL SANTRA

Good to see someone's starting to bother about this side of spectrum also. Green AI The computations required for deep learning research have been doubling every few months, resulting in an estimated 300,000x increase from 2012 to 2018.https://arxiv.org/abs/1907.10597 #NLProc @arXiv_Daily
0 replies, 1 likes


Jul 25 2019 Brian Merchant

"researchers should disclose how much computing power they use to encourage greener AI" Yep -- this is going to become a major issue as computationally heavy AI continues to ramp up and big tech builds out server farms to keep pace
0 replies, 1 likes


Aug 17 2019 Debanjan Mahata

A report (https://arxiv.org/pdf/1907.10597.pdf) from the @allen_ai argues that, for new models, energy efficiency is as important as accuracy. The report sets forth several ways to assess AI's carbon emissions. #AI #MachineLearning #NLProc
0 replies, 1 likes


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